How can action recognition be achieved?
This study from 2012 uses 3D convolutional neural networks (CNN) for automated recognition of human actions in surveillance videos. The 3D CNN model extracts features from both the spatial and the temporal dimensions by performing 3D convolutions, thereby capturing the motion information encoded in multiple adjacent frames. A very similar deep learning approach based on 3D CNN is demonstrated in the LIRIS and Orange Labs study from 2011.



This Oxford study from 2014 also uses a similar approach, but with two-stream CNN which incorporates spatial and temporal networks which can achieve good performance despite having limited training data. It recognises action from motion in the form of dense optical flow. For example:





Another study from 2007 demonstrates a method by detecting human falls based on a combination of motion history and human shape variation by analysing the video frames. It uses Motion History Image (MHI) to quantify the motion of the person.



Source: harishrithish7/Fall-Detection at GitHub



An alternative general approach could be action detection based on the posture using DNN. See: How to achieve recognition of postures and gestures?